✦
Muse AI
On-Device AI · Android · Fully Offline

Your phone. Your intelligence. Zero compromise.

Muse AI runs a complete large language model directly on your Android device — no servers, no subscriptions, no surveillance. Your data never leaves your hardware. And when other Muse devices are on the same Wi-Fi, they automatically discover each other and offload inference to the fastest available peer — zero setup.

▶ Open Beta · Android
🔒 0 bytes ever leave your device
0
% On-Device
All compute stays on your hardware
0 bytes
Sent to the cloud
No telemetry, no logging, ever
0
Languages
EN · FR · AR · RU · HI
Auto‑Mesh
Peer discovery
mDNS · same Wi-Fi · zero config
Capabilities

Everything runs on the metal.

No middleware, no API calls, no monthly bills. Muse executes inference directly through llama.cpp with ARM NEON intrinsics — the same stack that powers cutting-edge local AI on desktop, now in your pocket.

⚡
llama.cpp · ARM NEON
Quantized GGUF models run natively via ARM NEON / fp16 / dotprod vector instructions. Streaming token generation directly on the device CPU — no network round-trip.
Q4 · Q5 · Q8
🌐
Mesh Compute
Multiple devices running Muse on the same Wi-Fi auto-discover each other via mDNS. The orchestrator device routes jobs to the fastest available peer — automatically, no setup.
Zero Setup
🎙️
Offline Voice Mode
Full STT recognition pipeline plus Piper TTS synthesis — both running locally. Talk to Muse without internet, without Siri, without Google.
Piper TTS
👁️
On-Device Vision
Gemma 4 multimodal support with a 6 GB RAM gate. Analyse images, read documents, describe scenes — entirely on the device, zero network egress.
Gemma 4
🧠
Persistent Memory
Muse extracts and remembers facts across sessions. Preferences, names, context — stored in a local SQLite database and injected back into the prompt automatically.
Room DB
🔔
AI Notification Reply
Muse intercepts Android notifications and can draft contextual replies using local inference. Smart responses without sending a single character to a cloud.
Local NLU
⏰
On-Device Tasks & Reminders
Tell Muse naturally — "remind me to call Sarah tomorrow at 3pm" — and it parses intent, creates a reminder, and sets the alarm. All local. No Google Assistant, no Siri, no Alexa.
NLP → AlarmManager
🔒
No Account · No Subscription
Download the app, pick a model, start chatting. No email required, no payment, no usage limits tied to a server. The model you download is yours. Runs forever, offline, free.
Always Free
📦
Model Manager
Browse, download, resume, and switch GGUF models in-app. Import your own files via Android SAF. Supports small to large quantized models depending on your device RAM.
GGUF Hub
Why It Matters

Muse vs Cloud AI

Every cloud AI product asks you to trade something fundamental for convenience. Muse refuses that trade.

Capability Comparison
Illustrative scores across five dimensions — where cloud AI fails by design, not by accident
Muse AI
Cloud AI (avg)
Technical Stack
Every component runs in-process on the Android device — no sockets opened to external hosts
💬
Compose UI
Jetpack Compose · Material 3
Local
🧩
ViewModel + State
Kotlin Flow · Coroutines · Hilt
Local
⚡
JNI Bridge → llama.cpp
C++20 · ARM NEON · GGML backend
Native
💾
Persistence
Room · SQLite · DataStore
Local
🔊
Voice Pipeline
STT (on-device) · Piper TTS
Local
🌐
Mesh Network
mDNS · LAN socket · NSD API
Mesh
Mesh Inference

Your phone network becomes the compute cluster.

When you need more inference power, you don't need a server. Any other phone running Muse on the same Wi-Fi can take the job.

01
Auto-Discovery
When Worker Mode is enabled, Muse registers an mDNS service record on the local network. Any other Muse device on the same Wi-Fi discovers it within seconds — no IP address, no pairing, no QR code.
_lucyworker._tcp
02
Workload Negotiation
Each Worker device advertises its loaded model name and current tokens-per-second rate. The Orchestrator picks the fastest available peer for each job, with automatic retry on failure.
LAN socket · port 11435
03
Seamless Inference
Tokens stream back to the Orchestrator identically whether generated locally or remotely. If a Worker drops mid-stream, the job automatically retries on the next available peer, then falls back to local inference.
Auto-retry · local fallback
Not private by policy. Private by physics.

Cloud AI companies promise not to train on your data — and then change that policy. Muse can't make that promise, because there's no server to send data to. The constraint is architectural. Your conversations are stored in a local SQLite database on your device, never leaving your hardware.

🚫
No Server
🔐
Encrypted
👁️
No Telemetry